# Fba Simulator

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill fba-simulator -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill fba-simulator -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill fba-simulator -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill fba-simulator -g -a gemini-cli -y`

## Description

Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## Overview

The `fba-simulator` skill executes constraint-based metabolic simulations on
a validated COBRApy model. FBA solves a linear program to find the flux
distribution that maximizes (or minimizes) the objective function subject to
stoichiometric and thermodynamic constraints.

This skill sits between model construction (`gsmm-builder`) and biological
interpretation (`flux-analyzer`). All simulations are non-destructive: COBRApy
context managers restore model state after each perturbation.

---

## Workflow

### Step 1, Load Validated Model

```python
import cobra
import cobra.io
import cobra.flux_analysis
import pandas as pd

model = cobra.io.load_json_model("my_model.json")
print(f"Model: {model.id}  Solver: {model.solver}")
```

### Step 2, Standard FBA

FBA maximizes the objective (typically biomass) subject to stoichiometric
steady-state constraints: `S·v = 0`, `lb ≤ v ≤ ub`.

```python
# Run FBA
solution = model.optimize()

print(f"Status          : {solution.status}")
print(f"Growth rate     : {solution.objective_value:.4f} h^-1")
print(f"Glucose uptake  : "
      f"{solution.fluxes['EX_glc__D_e']:.4f} mmol/gDW/h")
print(f"O2 uptake       : "
      f"{solution.fluxes.get('EX_o2_e', 0):.4f} mmol/gDW/h")
print(f"Acetate sec.    : "
      f"{solution.fluxes.get('EX_ac_e', 0):.4f} mmol/gDW/h")

# Save full flux distribution
solution.fluxes.to_csv("fba_fluxes.csv", header=["flux_mmol_gDW_h"])
```

### Step 3, Parsimonious FBA (pFBA)

pFBA first maximizes growth, then minimizes total absolute flux, producing
the most "economical" solution consistent with maximum growth. This avoids
biologically unrealistic high-flux split cycles.

```python
pfba_solution = cobra.flux_analysis.pfba(model)

print(f"pFBA growth rate : {pfba_solution.objective_value:.4f} h^-1")
print(f"Total flux norm  : {pfba_solution.fluxes.abs().sum():.2f}")

pfba_solution.fluxes.to_csv("pfba_fluxes.csv", header=["flux_mmol_gDW_h"])
```

### Step 4, Flux Variability Analysis (FVA)

The rest: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/Biology-Agent/skills/fba-simulator/SKILL.md